code: def run_credit_scoring_problem(
      train_file_path,
      test_file_path,
      timeout: float = 5.0,
      visualization=False,
      target="target",
      **composer_args,
  ):
      automl = Fedot(
          problem="classification",
          timeout=timeout,
          preset="fast_train",
          logging_level=logging.FATAL,
          **composer_args,
      )
      automl.fit(train_file_path, target=target)
      automl.predict(test_file_path)
      metrics = automl.get_metrics()
  
      if automl.history and automl.history.generations:
          print(automl.history.get_leaderboard())
  
      if visualization:
          automl.current_pipeline.show()
  
      print(f'Composed ROC AUC is {round(metrics["roc_auc_pen"], 3)}')
  
      return metrics["roc_auc_pen"]
  
file_path: FEDOT/cases/credit_scoring/credit_scoring_problem.py
imports: import logging
  from sklearn.metrics import roc_auc_score
  from fedot import Fedot
  from fedot.core.data.data import InputData
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.utils import fedot_project_root
  from fedot.core.utils import set_random_seed
label_desc: Use image node_id 2 for calling a global function with example usage: run_credit_scoring_problem(train_file_path, test_file_path, timeout, visualization, target) and returns: metrics